arXiv subjects

Mathias El Baz

Publications and source records attributed to Mathias El Baz.

4 recordsLinked to original sources

arXiv2026

Flow-Based Surrogates for High-Dimensional Likelihoods in Experimental Neutrino Physics

Precision long-baseline neutrino experiments use near-detector data to constrain systematic uncertainties on the unoscillated neutrino flux, a prerequisite for accurate oscillation parameter measurements at the far detector. When the constrained likelihood is high-dimensional and non-Gaussian, this procedure demands advanced statistical treatment. Here we show that normalizing flows provide faithful and portable likelihood models for this problem. Leveraging an initial Gaussian approximation of the likelihood, we train a hybrid architecture combining coupling transformations and autoregressive spline flows. We demonstrate the method on a representative near-detector likelihood replica with 110 systematic uncertainty parameters, 10 of which explicitly introduce non-Gaussianities in the posterior. The trained model achieves a relative effective sample size of 98%, compared with about 5% for the Gaussian approximation, and reproduces a Markov chain Monte Carlo reference while remaining closed-form, samplable, and pointwise evaluable, making it suited to downstream uncertainty propagation and future near-detector to far-detector fits.

hep-ex↗

arXiv2026

Azimuthal asymmetry in exclusive quasi-elastic neutrino-nucleus interactions

In neutrino oscillation experiments, exclusive measurements of neutrino-nucleus interactions play a critical role, by providing the theoretical and experimental input needed for a reliable estimation of the neutrino energy. In this paper, we derive the general form of the azimuthal angle distribution for quasi-elastic scattering, focusing on a dependency that has been routinely overlooked. We demonstrate that the outgoing nucleon exhibits a preference for emission outside the lepton scattering plane, with an asymmetric azimuthal distribution. In the context of neutrino-nucleus scattering, we argue that this asymmetry is caused by parity violation in the weak interaction. Furthermore, we show that in cross section calculations the asymmetry is sensitive to nuclear modeling choices and to the shell structure of the initial nucleus, thus providing a novel source of information for energy reconstruction in neutrino experiments. We study the experimental feasibility of observing this effect by applying a realistic momentum detection threshold and an intranuclear cascade. We estimate that the asymmetry is observable with $\mathcal{O}$($10^4$) events at the 99% confidence level for neutrino interactions on $^{12}$C, suggesting that the effect is within reach of the current generation of neutrino detectors.

nucl-th↗

arXiv2025

Efficient Monte Carlo Event Generation for Neutrino-Nucleus Exclusive Cross Sections

Modern neutrino-nucleus cross section predictions need to incorporate sophisticated nuclear models to achieve greater predictive precision. However, the computational complexity of these advanced models often limits their practicality for experimental analyses. To address this challenge, we introduce a new Monte Carlo method utilizing Normalizing Flows to generate surrogate cross sections that closely approximate those of the original model while significantly reducing computational overhead. As a case study, we built a Monte Carlo event generator for the neutrino-nucleus cross section model developed by the Ghent group. This model employs a Hartree-Fock procedure to establish a quantum mechanical framework in which both the bound and scattering nucleon states are solutions to the mean-field nuclear potential. The surrogate cross sections generated by our method demonstrate excellent accuracy with a relative effective sample size of more than $98.4 \%$, providing a computationally efficient alternative to traditional Monte Carlo sampling methods for differential cross sections.

hep-ex↗

arXiv2024

Fast Posterior Probability Sampling with Normalizing Flows and Its Applicability in Bayesian analysis in Particle Physics

In this study, we use Rational-Quadratic Neural Spline Flows, a sophisticated parametrization of Normalizing Flows, for inferring posterior probability distributions in scenarios where direct evaluation of the likelihood is challenging at inference time. We exemplify this approach using the T2K near detector as a working example, focusing on learning the posterior probability distribution of neutrino flux binned in neutrino energy. The predictions of the trained model are conditioned at inference time by the momentum and angle of the outgoing muons released after neutrino-nuclei interaction. This conditioning allows for the generation of personalized posterior distributions, tailored to the muon observables, all without necessitating a full retraining of the model for each new dataset. The performances of the model are studied for different shapes of the posterior distributions.

physics.data-an↗